Best Data Science Certifications

Here is the uncomfortable truth about most "best data science certifications" lists: half of the entries on them are not certifications at all. The IBM Data Science Professional Certificate and the Google Data Analytics Professional Certificate are course-completion credentials — you finish graded coursework on Coursera and receive a certificate. A certification, by contrast, is a proctored exam you can fail: Microsoft's DP-100, the Databricks machine learning exams, SAS's AI and machine learning credentials. Employers read the two categories differently, and choosing well starts with knowing which category you actually need.

This article ranks and compares credentials specifically for the data scientist and machine learning practitioner role — model building, experimentation, ML deployment. If you are weighing up analyst credentials instead, the best data analyst certifications list covers that lane, and if you want to see every data role side by side before committing, start with the best certifications for data professionals overview.

What actually makes a data science certification "best"?

There is no single best data science certification, because the credential that helps a self-taught Python programmer break in is different from the one that helps a working analyst move into machine learning. A useful ranking judges each option on five things: what it proves (coursework completed versus an exam passed), whose hiring managers recognise it, how well it matches the tools your target employers use, what it costs in money and study time, and what it sets you up for next.

One market signal worth knowing before you spend anything: US Bureau of Labor Statistics figures for 2024 (as cited in Coursera's data analytics certification guide, updated July 2026) put median weekly earnings for US workers holding a certification but no licence at $1,566 with 3.3% unemployment, against $1,131 and 4.9% for workers with neither. That is general workforce data, not a data-science-specific promise — but it is consistent with the common-sense view that verified credentials correlate with better outcomes. Treat any page quoting a precise "average data science certification salary" with suspicion; providers do not publish one, and pay varies enormously by country, city, experience and role.

The two credential types, and why the distinction matters

Proctored certifications — Microsoft, Databricks, SAS, Google Cloud exams — are timed, invigilated tests. You register, pay an exam fee, and either pass or you don't. They carry more weight as verification: a recruiter knows an independent examiner stood between you and the badge.

Professional certificates — IBM's and Google's Coursera programmes — are structured training. The IBM Data Science Professional Certificate, for example, is a sequence of graded courses covering Python, statistics and machine learning basics, not a proctored exam. That makes it weaker as proof but often stronger as preparation: if you cannot yet write the Python that a certification exam assumes, a coursework certificate is frequently the right first purchase, not the exam.

A sensible career plan often uses both, in that order. How to sequence them — which to take first, second and third — is the subject of our companion data science certification roadmap, so this article stays focused on which individual credentials are worth shortlisting.

The shortlist: certifications worth a data scientist's time

Microsoft Certified: Azure Data Scientist Associate (DP-100)

The DP-100 exam — Designing and Implementing a Data Science Solution on Azure — leads to Microsoft's Azure Data Scientist Associate certification. It sits squarely in this article's lane: it tests running machine learning workloads on Azure, from experiment setup through model training and deployment, rather than dashboarding or data pipelines.

Its strongest argument is Microsoft's enterprise footprint. If the companies you are targeting run on Azure — and a very large share of enterprises do — an Azure-stamped ML credential maps directly onto the job. Its main caveat is the same as any vendor exam: it certifies machine learning on Azure, and assumes you already have working Python and ML fundamentals rather than teaching them.

Microsoft revises and occasionally retires exams as its platform evolves (its analytics line has already seen DP-500 give way to DP-600, and DP-203 to DP-700, in the shift to Microsoft Fabric), so confirm the current DP-100 objectives, fee for your country and scheduling details on Microsoft Learn before booking. When you are ready to gauge readiness, working through DP-100 practice questions against the current objective list will show you which Azure ML domains still need work.

Best for: practitioners targeting Azure-based employers who already have Python and ML basics.

Databricks Certified Machine Learning Professional

Databricks has become one of the default platforms for production machine learning, and its Certified Machine Learning Professional credential targets exactly the skill set data science teams struggle to hire for: taking models beyond the notebook into tracked, deployed, monitored production workflows. Databricks also offers an associate-level machine learning exam beneath it; check the current lineup, prerequisites and pricing on the Databricks Academy site, as exam details change and are not something to take from third-party summaries.

Best for: data scientists at (or targeting) organisations standardised on Databricks and Spark, especially those moving towards ML engineering.

Google Cloud Professional Machine Learning Engineer

Google Cloud's professional-level ML certification is the GCP counterpart to DP-100 — designing, building and productionising ML solutions on Google Cloud. It is generally regarded as one of the more demanding cloud ML exams, and it makes the shortlist for the same reason the Azure credential does: cloud-platform ML certifications are what employers most often name in data science job adverts. Format, cost and prerequisites should be confirmed on Google Cloud's certification pages, which are the authoritative source for current details.

Best for: ML practitioners in GCP shops, or experienced data scientists who want the hardest-hitting cloud ML badge relevant to their stack.

SAS AI and Machine Learning Professional

SAS remains deeply embedded in banking, insurance, pharmaceuticals and government analytics. Its AI and machine learning professional credentials — such as the SAS Certified AI and Machine Learning Professional track — matter in those sectors in a way generic lists underrate. If your target industry runs on SAS, this is a differentiator; if it doesn't, skip it without guilt. Current exam structure and fees are listed on SAS's certification site.

Best for: data scientists heading into regulated industries where SAS is entrenched.

IBM Data Science Professional Certificate (Coursera)

The IBM programme is the best-known coursework credential for aspiring data scientists: a multi-course Coursera sequence teaching Python, data analysis, visualisation and introductory machine learning from zero. Remember what it is — graded coursework, not a proctored exam — and position it accordingly on your CV ("professional certificate", not "certified"). For career changers with no programming background it is often the most rational first step on the way to the exams above; IBM also offers proctored specialist exams, such as its AI Enterprise Workflow data science specialist credential, for later in the journey. Current pricing and course lists are on Coursera.

Best for: complete beginners who need structured training before any exam is realistic.